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Deep comparisons of Neural Networks from the EEGNet family
Most of the Brain-Computer Interface (BCI) publications, which propose
artificial neural networks for Motor Imagery (MI) Electroencephalography (EEG)
signal classification, are presented using one of the BCI Competition datasets.
However, these databases contain MI EEG data from less than or equal to 10
subjects . In addition, these algorithms usually include only bandpass
filtering to reduce noise and increase signal quality. In this article, we
compared 5 well-known neural networks (Shallow ConvNet, Deep ConvNet, EEGNet,
EEGNet Fusion, MI-EEGNet) using open-access databases with many subjects next
to the BCI Competition 4 2a dataset to acquire statistically significant
results. We removed artifacts from the EEG using the FASTER algorithm as a
signal processing step. Moreover, we investigated whether transfer learning can
further improve the classification results on artifact filtered data. We aimed
to rank the neural networks; therefore, next to the classification accuracy, we
introduced two additional metrics: the accuracy improvement from chance level
and the effect of transfer learning. The former can be used with different
class-numbered databases, while the latter can highlight neural networks with
sufficient generalization abilities. Our metrics showed that the researchers
should not avoid Shallow ConvNet and Deep ConvNet because they can perform
better than the later published ones from the EEGNet family
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